#geospatial data

TorchGeo provides specialized resources including datasets, samplers, and models for geospatial data processing. It bridges the gap between machine learning and geospatial data, facilitating tasks such as image classification and semantic segmentation. Integrated with PyTorch, it allows seamless incorporation into workflows, while its support for pre-trained weights accommodates multispectral imaging needs. Compatibility with tools like Lightning enhances its use in reproducible and experimental workflows, positioning TorchGeo as an essential tool for geospatial deep learning.

TerraTorch enhances geospatial data modeling by utilizing pre-trained model backbones like Prithvi, SatMAE, and ScaleMAE, supporting tasks including segmentation, classification, and regression. Its flexible setups through pip or conda and adaptable configuration options facilitate simple integration for developers extending geospatial functionality.

The segment-geospatial Python package facilitates geospatial data analysis with the Segment Anything Model (SAM), reducing the need for extensive coding. This package is available on PyPI and conda-forge. It allows for downloading map tiles, segmenting GeoTIFF files, and creating interactive markers for remote sensing imagery. Users can visualize output, save data in multiple vector formats, and utilize text and box prompts. The package offers straightforward installation and optional GPU acceleration, making it a valuable resource for researchers in earth observation using deep learning.